{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入数据探索工具包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#从磁盘上读取数据集\n",
    "data = pd.read_csv(\"D:\\python project\\Test1\\day.csv\")\n",
    "#获取前5项数据\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取数据集的行和列数\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#获取各个特征值的数据类型\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取各个特征值的平均值、标准差、最小值、前1/4的值、中间值、前3/4值、最大值\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看下当前数据集上是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看两两特征相关性\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "data = data.drop('instant', axis = 1)  #编号不能作为预测输出值的特征值\n",
    "cols = data.columns\n",
    "data_corr = data.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从原始数据离特征值X和输出y\n",
    "y = data['cnt'].values\n",
    "X = data.drop(['dteday','casual','registered','cnt'], axis = 1)  #由于只需要对总的共享单车进行数据预测，casual,registered两项可不需要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 11)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据分离\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#随机选择80%作为训练集，其余20%作为测试集\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.2, random_state = 33)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化处理\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "#分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.fit_transform(X_test)\n",
    "\n",
    "#对输出做标准化处理\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.fit_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用RMSE评价最小二乘线性回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LinearRegression on train is:  0.4553504584919946\n"
     ]
    }
   ],
   "source": [
    "#最小二乘线性回归模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#配置初始化\n",
    "lr = LinearRegression()\n",
    "#训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "#预测\n",
    "y_train_predict = lr.predict(X_train)\n",
    "y_test_predict = lr.predict(X_test)\n",
    "\n",
    "# 使用RMSE评价模型在训练集上的性能，并输出评估结果\n",
    "print(\"The RMSE of LinearRegression on train is: \", np.sqrt(mean_squared_error(y_train, y_train_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用RMSE评价岭回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LinearRegression on train is:  0.4555970198410895\n"
     ]
    }
   ],
   "source": [
    "#岭回归模型\n",
    "from sklearn.linear_model import RidgeCV\n",
    "#配置初始化\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "rcv = RidgeCV(alphas = alphas, cv = 5)\n",
    "#训练模式参数\n",
    "rcv.fit(X_train, y_train)\n",
    "#预测\n",
    "y_train_Ridge_predict = rcv.predict(X_train)\n",
    "y_test_Ridge_predict = rcv.predict(X_test)\n",
    "\n",
    "# 使用RMSE评价模型在训练集上的性能，并输出评估结果\n",
    "print(\"The RMSE of LinearRegression on train is: \", np.sqrt(mean_squared_error(y_train, y_train_Ridge_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用RMSE评价Lasso回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LinearRegression on train is:  0.45737690335692516\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#Lasso模型\n",
    "from sklearn.linear_model import LassoCV\n",
    "#配置初始化\n",
    "lcv = LassoCV(alphas = alphas, cv = 5)\n",
    "#训练模式参数\n",
    "lcv.fit(X_train, y_train)\n",
    "#预测\n",
    "y_train_Lasso_predict = lcv.predict(X_train)\n",
    "y_test_Lasso_predict = lcv.predict(X_test)\n",
    "\n",
    "# 使用RMSE评价模型在训练集上的性能，并输出评估结果\n",
    "print(\"The RMSE of LinearRegression on train is: \", np.sqrt(mean_squared_error(y_train, y_train_Lasso_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 最小二乘线性模型得到的各特征的系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>[0.5324153075686427]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>[0.3029648563642086]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.2629254550745269]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.2222783379371292]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.07004159630308603]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.044095329630587446]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.028875968573428674]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.060415966935772655]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>[-0.08635615066159807]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.09222316307072856]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>[-0.19330140643417207]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       columns                     coef\n",
       "1           yr     [0.5324153075686427]\n",
       "0       season     [0.3029648563642086]\n",
       "8        atemp     [0.2629254550745269]\n",
       "7         temp     [0.2222783379371292]\n",
       "4      weekday    [0.07004159630308603]\n",
       "5   workingday   [0.044095329630587446]\n",
       "3      holiday  [-0.028875968573428674]\n",
       "9          hum  [-0.060415966935772655]\n",
       "2         mnth   [-0.08635615066159807]\n",
       "10   windspeed   [-0.09222316307072856]\n",
       "6   weathersit   [-0.19330140643417207]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "columns = X.columns\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 岭回归模型得到的各特征的系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>[0.5230344147953434]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>[0.2820480638200241]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.25034441111067357]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.2342001499198458]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.06765302057283533]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.043721373551396006]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.02975845859934594]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.06206158987872586]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>[-0.06677593980192069]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.09274902100980804]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>[-0.18990466056652433]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       columns                    coef\n",
       "1           yr    [0.5230344147953434]\n",
       "0       season    [0.2820480638200241]\n",
       "8        atemp   [0.25034441111067357]\n",
       "7         temp    [0.2342001499198458]\n",
       "4      weekday   [0.06765302057283533]\n",
       "5   workingday  [0.043721373551396006]\n",
       "3      holiday  [-0.02975845859934594]\n",
       "9          hum  [-0.06206158987872586]\n",
       "2         mnth  [-0.06677593980192069]\n",
       "10   windspeed  [-0.09274902100980804]\n",
       "6   weathersit  [-0.18990466056652433]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((rcv.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso回归模型得到的各特征的系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.522253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.269194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.238309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.214662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weekday</td>\n",
       "      <td>0.058887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>workingday</td>\n",
       "      <td>0.037327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>-0.021166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-0.025127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hum</td>\n",
       "      <td>-0.051614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-0.082284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>-0.190071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       columns      coef\n",
       "1           yr  0.522253\n",
       "8        atemp  0.269194\n",
       "0       season  0.238309\n",
       "7         temp  0.214662\n",
       "4      weekday  0.058887\n",
       "5   workingday  0.037327\n",
       "2         mnth -0.021166\n",
       "3      holiday -0.025127\n",
       "9          hum -0.051614\n",
       "10   windspeed -0.082284\n",
       "6   weathersit -0.190071"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lcv.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三种模式在测试集上的r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小二乘回归模型在测试集上的r2_score is  0.8259692806252051\n",
      "岭回归模型在测试集上的r2_score is        0.8270499308875108\n",
      "Lasso回归模型在测试集上的r2_score is     0.8260911342059255\n"
     ]
    }
   ],
   "source": [
    "print(\"最小二乘回归模型在测试集上的r2_score is \", r2_score(y_test, y_test_predict))\n",
    "print(\"岭回归模型在测试集上的r2_score is       \", r2_score(y_test, y_test_Ridge_predict))\n",
    "print(\"Lasso回归模型在测试集上的r2_score is    \", r2_score(y_test, y_test_Lasso_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由以上模型在测试集上的r2_score，性能：岭回归模型优于Lasso回归模型，Lasso回归模型优于最小二乘回归模型。在此测试集上，跟正则相有关，模型越复杂，性能越好。\n"
   ]
  }
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